High-Fidelity Image Reconstruction for Multichannel SAR With Nonuniform Sampling in Composite Jamming Environments

Abstract

In practical complex electromagnetic environments, synthetic aperture radar (SAR) systems are susceptible to composite jamming, encompassing both blocking and deceptive jamming. Concurrently, the imaging quality is further degraded by nonuniform sampling inherent in multichannel architectures. To address these dual challenges, this article proposes a joint robust space-time processing and adaptive regularization reconstruction method for multichannel SAR. The proposed approach is designed to simultaneously suppress composite jamming and compensate for nonuniform sampling effects. First, for received signals containing both target echoes and jamming components, a robust space-time composite jamming suppression algorithm is developed. The algorithm reconstructs the covariance matrices and steering vectors associated with the composite jamming and targets, and subsequently designs a robust oblique projection matrix for mainlobe jamming suppression without compromising target signal integrity. Furthermore, to overcome the ill-conditioned observation matrix problem caused by residual sidelobe jamming and nonuniform sampling, an adaptive regularization signal reconstruction method is introduced. This method formulates a joint optimization model that integrates residual jamming suppression with signal fidelity constraints. By adaptively adjusting the regularization parameters, a dynamic balance between jamming suppression and high-fidelity signal reconstruction is achieved, thereby significantly enhancing the robustness of the overall process. Simulation experiments and measured SAR data validate that the proposed method effectively suppresses composite jamming and achieves accurate signal reconstruction under nonuniform sampling conditions.